Overview

Dataset statistics

Number of variables12
Number of observations98818
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 MiB
Average record size in memory149.8 B

Variable types

Numeric11
Categorical1

Warnings

df_index is highly correlated with friend_count and 1 other fieldsHigh correlation
friend_count is highly correlated with df_index and 1 other fieldsHigh correlation
friendships_initiated is highly correlated with df_index and 1 other fieldsHigh correlation
likes is highly correlated with mobile_likes and 1 other fieldsHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes is highly correlated with likesHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
df_index is highly correlated with friend_count and 4 other fieldsHigh correlation
friend_count is highly correlated with df_index and 4 other fieldsHigh correlation
friendships_initiated is highly correlated with df_index and 3 other fieldsHigh correlation
likes is highly correlated with likes_received and 4 other fieldsHigh correlation
likes_received is highly correlated with df_index and 6 other fieldsHigh correlation
mobile_likes is highly correlated with likes and 3 other fieldsHigh correlation
mobile_likes_received is highly correlated with df_index and 6 other fieldsHigh correlation
www_likes is highly correlated with likes and 1 other fieldsHigh correlation
www_likes_received is highly correlated with df_index and 6 other fieldsHigh correlation
df_index is highly correlated with friend_count and 1 other fieldsHigh correlation
friend_count is highly correlated with df_index and 1 other fieldsHigh correlation
friendships_initiated is highly correlated with df_index and 1 other fieldsHigh correlation
likes is highly correlated with likes_received and 3 other fieldsHigh correlation
likes_received is highly correlated with likes and 3 other fieldsHigh correlation
mobile_likes is highly correlated with likes and 2 other fieldsHigh correlation
mobile_likes_received is highly correlated with likes and 3 other fieldsHigh correlation
www_likes_received is highly correlated with likes and 2 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
friendships_initiated is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with friendships_initiated and 1 other fieldsHigh correlation
mobile_likes is highly correlated with likesHigh correlation
friend_count is highly correlated with friendships_initiated and 1 other fieldsHigh correlation
likes is highly correlated with www_likes and 1 other fieldsHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
likes_received is highly skewed (γ1 = 112.0109727) Skewed
mobile_likes_received is highly skewed (γ1 = 107.4678309) Skewed
www_likes_received is highly skewed (γ1 = 126.1856832) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
friend_count has 1956 (2.0%) zeros Zeros
friendships_initiated has 2988 (3.0%) zeros Zeros
likes has 22277 (22.5%) zeros Zeros
likes_received has 24392 (24.7%) zeros Zeros
mobile_likes has 34994 (35.4%) zeros Zeros
mobile_likes_received has 29956 (30.3%) zeros Zeros
www_likes has 60927 (61.7%) zeros Zeros
www_likes_received has 36817 (37.3%) zeros Zeros

Reproduction

Analysis started2021-08-17 07:02:49.834101
Analysis finished2021-08-17 07:03:14.957965
Duration25.12 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct98818
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49501.35276
Minimum0
Maximum99002
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:15.075640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4950.85
Q124747.25
median49506.5
Q374253.75
95-th percentile94054.15
Maximum99002
Range99002
Interquartile range (IQR)49506.5

Descriptive statistics

Standard deviation28580.82261
Coefficient of variation (CV)0.5773745769
Kurtosis-1.200157688
Mean49501.35276
Median Absolute Deviation (MAD)24753.5
Skewness-8.232036957 × 10-5
Sum4891624677
Variance816863420.8
MonotonicityStrictly increasing
2021-08-17T12:33:15.234226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
853791
 
< 0.1%
75291
 
< 0.1%
54801
 
< 0.1%
280071
 
< 0.1%
259581
 
< 0.1%
321011
 
< 0.1%
300521
 
< 0.1%
198111
 
< 0.1%
177621
 
< 0.1%
Other values (98808)98808
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
990021
< 0.1%
990011
< 0.1%
990001
< 0.1%
989991
< 0.1%
989981
< 0.1%
989971
< 0.1%
989961
< 0.1%
989951
< 0.1%
989941
< 0.1%
989931
< 0.1%

age
Real number (ℝ≥0)

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.89545427
Minimum13
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:15.391801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median28
Q345
95-th percentile68
Maximum95
Range82
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.35955148
Coefficient of variation (CV)0.5121498399
Kurtosis0.3833371448
Mean33.89545427
Median Absolute Deviation (MAD)9
Skewness1.082567963
Sum3349481
Variance301.3540275
MonotonicityNot monotonic
2021-08-17T12:33:15.523448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286621
 
6.7%
185196
 
5.3%
234401
 
4.5%
194390
 
4.4%
203768
 
3.8%
213670
 
3.7%
253631
 
3.7%
173281
 
3.3%
163086
 
3.1%
223032
 
3.1%
Other values (73)57742
58.4%
ValueCountFrequency (%)
13484
 
0.5%
141925
 
1.9%
152617
2.6%
163086
3.1%
173281
3.3%
185196
5.3%
194390
4.4%
203768
3.8%
213670
3.7%
223032
3.1%
ValueCountFrequency (%)
9576
 
0.1%
94181
0.2%
93202
0.2%
9250
 
0.1%
9174
 
0.1%
9068
 
0.1%
8958
 
0.1%
8860
 
0.1%
8741
 
< 0.1%
8675
 
0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
male
58566 
female
40252 

Length

Max length6
Median length4
Mean length4.814669392
Min length4

Characters and Unicode

Total characters475776
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male58566
59.3%
female40252
40.7%

Length

2021-08-17T12:33:15.810675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-17T12:33:15.907418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male58566
59.3%
female40252
40.7%

Most occurring characters

ValueCountFrequency (%)
e139070
29.2%
m98818
20.8%
a98818
20.8%
l98818
20.8%
f40252
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter475776
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e139070
29.2%
m98818
20.8%
a98818
20.8%
l98818
20.8%
f40252
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin475776
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e139070
29.2%
m98818
20.8%
a98818
20.8%
l98818
20.8%
f40252
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII475776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e139070
29.2%
m98818
20.8%
a98818
20.8%
l98818
20.8%
f40252
 
8.5%

tenure
Real number (ℝ≥0)

Distinct2418
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean535.6817887
Minimum0
Maximum3139
Zeros69
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:16.011110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1226
median412
Q3673
95-th percentile1567
Maximum3139
Range3139
Interquartile range (IQR)447

Descriptive statistics

Standard deviation454.2589293
Coefficient of variation (CV)0.8480014419
Kurtosis2.195246589
Mean535.6817887
Median Absolute Deviation (MAD)212
Skewness1.530829395
Sum52935003
Variance206351.1748
MonotonicityNot monotonic
2021-08-17T12:33:16.160774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300173
 
0.2%
303170
 
0.2%
242163
 
0.2%
272163
 
0.2%
257161
 
0.2%
297161
 
0.2%
280160
 
0.2%
285160
 
0.2%
284158
 
0.2%
278158
 
0.2%
Other values (2408)97191
98.4%
ValueCountFrequency (%)
069
0.1%
160
0.1%
272
0.1%
379
0.1%
486
0.1%
592
0.1%
693
0.1%
784
0.1%
887
0.1%
993
0.1%
ValueCountFrequency (%)
31393
< 0.1%
31291
 
< 0.1%
31281
 
< 0.1%
31011
 
< 0.1%
30191
 
< 0.1%
29581
 
< 0.1%
29261
 
< 0.1%
28881
 
< 0.1%
28221
 
< 0.1%
27881
 
< 0.1%

friend_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2561
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3898986
Minimum0
Maximum4923
Zeros1956
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:16.319391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131
median82
Q3206
95-th percentile720
Maximum4923
Range4923
Interquartile range (IQR)175

Descriptive statistics

Standard deviation387.4751451
Coefficient of variation (CV)1.972989181
Kurtosis50.08132589
Mean196.3898986
Median Absolute Deviation (MAD)64
Skewness6.058962436
Sum19406857
Variance150136.9881
MonotonicityNot monotonic
2021-08-17T12:33:16.469395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01956
 
2.0%
11814
 
1.8%
21115
 
1.1%
3860
 
0.9%
5785
 
0.8%
4747
 
0.8%
10737
 
0.7%
24732
 
0.7%
6720
 
0.7%
29718
 
0.7%
Other values (2551)88634
89.7%
ValueCountFrequency (%)
01956
2.0%
11814
1.8%
21115
1.1%
3860
0.9%
4747
 
0.8%
5785
0.8%
6720
 
0.7%
7670
 
0.7%
8718
 
0.7%
9698
 
0.7%
ValueCountFrequency (%)
49231
< 0.1%
49171
< 0.1%
48631
< 0.1%
48451
< 0.1%
48441
< 0.1%
48261
< 0.1%
48171
< 0.1%
48031
< 0.1%
47971
< 0.1%
47941
< 0.1%

friendships_initiated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1519
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.4887268
Minimum0
Maximum4144
Zeros2988
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:16.620274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median46
Q3117
95-th percentile418
Maximum4144
Range4144
Interquartile range (IQR)100

Descriptive statistics

Standard deviation188.8667665
Coefficient of variation (CV)1.757084414
Kurtosis42.52974172
Mean107.4887268
Median Absolute Deviation (MAD)36
Skewness5.151078708
Sum10621821
Variance35670.65547
MonotonicityNot monotonic
2021-08-17T12:33:16.765217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02988
 
3.0%
12209
 
2.2%
21546
 
1.6%
31354
 
1.4%
41348
 
1.4%
61325
 
1.3%
51325
 
1.3%
111317
 
1.3%
81312
 
1.3%
131276
 
1.3%
Other values (1509)82818
83.8%
ValueCountFrequency (%)
02988
3.0%
12209
2.2%
21546
1.6%
31354
1.4%
41348
1.4%
51325
1.3%
61325
1.3%
71234
1.2%
81312
1.3%
91243
1.3%
ValueCountFrequency (%)
41441
< 0.1%
36541
< 0.1%
35941
< 0.1%
35381
< 0.1%
34151
< 0.1%
32381
< 0.1%
32331
< 0.1%
30861
< 0.1%
30781
< 0.1%
30241
< 0.1%

likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2921
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.1243498
Minimum0
Maximum25111
Zeros22277
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:16.917409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q381
95-th percentile726.15
Maximum25111
Range25111
Interquartile range (IQR)80

Descriptive statistics

Standard deviation572.5748897
Coefficient of variation (CV)3.667428497
Kurtosis200.3880019
Mean156.1243498
Median Absolute Deviation (MAD)11
Skewness11.02377153
Sum15427896
Variance327842.0043
MonotonicityNot monotonic
2021-08-17T12:33:17.073325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022277
22.5%
16916
 
7.0%
24428
 
4.5%
33235
 
3.3%
42503
 
2.5%
52025
 
2.0%
61804
 
1.8%
71615
 
1.6%
81430
 
1.4%
91379
 
1.4%
Other values (2911)51206
51.8%
ValueCountFrequency (%)
022277
22.5%
16916
 
7.0%
24428
 
4.5%
33235
 
3.3%
42503
 
2.5%
52025
 
2.0%
61804
 
1.8%
71615
 
1.6%
81430
 
1.4%
91379
 
1.4%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
165831
< 0.1%
147991
< 0.1%
143551
< 0.1%
140501
< 0.1%
140391
< 0.1%
136921
< 0.1%
136221
< 0.1%

likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2675
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.6769414
Minimum0
Maximum261197
Zeros24392
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:17.225374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q359
95-th percentile561
Maximum261197
Range261197
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1389.045639
Coefficient of variation (CV)9.735600052
Kurtosis17361.07811
Mean142.6769414
Median Absolute Deviation (MAD)8
Skewness112.0109727
Sum14099050
Variance1929447.786
MonotonicityNot monotonic
2021-08-17T12:33:17.377412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024392
24.7%
17291
 
7.4%
24537
 
4.6%
33342
 
3.4%
42663
 
2.7%
52367
 
2.4%
61868
 
1.9%
71678
 
1.7%
81535
 
1.6%
91349
 
1.4%
Other values (2665)47796
48.4%
ValueCountFrequency (%)
024392
24.7%
17291
 
7.4%
24537
 
4.6%
33342
 
3.4%
42663
 
2.7%
52367
 
2.4%
61868
 
1.9%
71678
 
1.7%
81535
 
1.6%
91349
 
1.4%
ValueCountFrequency (%)
2611971
< 0.1%
1781661
< 0.1%
1520141
< 0.1%
1060251
< 0.1%
826231
< 0.1%
535341
< 0.1%
529641
< 0.1%
456331
< 0.1%
424491
< 0.1%
395361
< 0.1%

mobile_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2394
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1564391
Minimum0
Maximum25111
Zeros34994
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:17.524420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q346
95-th percentile482
Maximum25111
Range25111
Interquartile range (IQR)46

Descriptive statistics

Standard deviation445.511712
Coefficient of variation (CV)4.19674695
Kurtosis360.808137
Mean106.1564391
Median Absolute Deviation (MAD)4
Skewness14.16034567
Sum10490167
Variance198480.6856
MonotonicityNot monotonic
2021-08-17T12:33:17.673390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034994
35.4%
16287
 
6.4%
23930
 
4.0%
32910
 
2.9%
42262
 
2.3%
51790
 
1.8%
61597
 
1.6%
71395
 
1.4%
81210
 
1.2%
91148
 
1.2%
Other values (2384)41295
41.8%
ValueCountFrequency (%)
034994
35.4%
16287
 
6.4%
23930
 
4.0%
32910
 
2.9%
42262
 
2.3%
51790
 
1.8%
61597
 
1.6%
71395
 
1.4%
81210
 
1.2%
91148
 
1.2%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
140391
< 0.1%
135291
< 0.1%
129341
< 0.1%
126391
< 0.1%
121041
< 0.1%
120831
< 0.1%
119591
< 0.1%

mobile_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2002
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.12564513
Minimum0
Maximum138561
Zeros29956
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:17.820387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile317
Maximum138561
Range138561
Interquartile range (IQR)33

Descriptive statistics

Standard deviation840.577049
Coefficient of variation (CV)9.991923958
Kurtosis15500.8792
Mean84.12564513
Median Absolute Deviation (MAD)4
Skewness107.4678309
Sum8313128
Variance706569.7754
MonotonicityNot monotonic
2021-08-17T12:33:17.961385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029956
30.3%
18227
 
8.3%
24942
 
5.0%
33598
 
3.6%
42936
 
3.0%
52382
 
2.4%
62017
 
2.0%
71744
 
1.8%
81520
 
1.5%
91433
 
1.5%
Other values (1992)40063
40.5%
ValueCountFrequency (%)
029956
30.3%
18227
 
8.3%
24942
 
5.0%
33598
 
3.6%
42936
 
3.0%
52382
 
2.4%
62017
 
2.0%
71744
 
1.8%
81520
 
1.5%
91433
 
1.5%
ValueCountFrequency (%)
1385611
< 0.1%
1312441
< 0.1%
899111
< 0.1%
733331
< 0.1%
434101
< 0.1%
307541
< 0.1%
303871
< 0.1%
273531
< 0.1%
207701
< 0.1%
189251
< 0.1%

www_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1724
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.9679107
Minimum0
Maximum14865
Zeros60927
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:18.129364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile208
Maximum14865
Range14865
Interquartile range (IQR)7

Descriptive statistics

Standard deviation285.7627019
Coefficient of variation (CV)5.718924362
Kurtosis448.7068585
Mean49.9679107
Median Absolute Deviation (MAD)0
Skewness16.90545084
Sum4937729
Variance81660.32178
MonotonicityNot monotonic
2021-08-17T12:33:18.276413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060927
61.7%
14678
 
4.7%
22750
 
2.8%
31945
 
2.0%
41415
 
1.4%
51201
 
1.2%
61075
 
1.1%
7895
 
0.9%
8790
 
0.8%
9755
 
0.8%
Other values (1714)22387
 
22.7%
ValueCountFrequency (%)
060927
61.7%
14678
 
4.7%
22750
 
2.8%
31945
 
2.0%
41415
 
1.4%
51201
 
1.2%
61075
 
1.1%
7895
 
0.9%
8790
 
0.8%
9755
 
0.8%
ValueCountFrequency (%)
148651
< 0.1%
129031
< 0.1%
110771
< 0.1%
107631
< 0.1%
106271
< 0.1%
105391
< 0.1%
102551
< 0.1%
102321
< 0.1%
99021
< 0.1%
94311
< 0.1%

www_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1634
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.55129632
Minimum0
Maximum129953
Zeros36817
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size772.1 KiB
2021-08-17T12:33:18.433363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile227
Maximum129953
Range129953
Interquartile range (IQR)20

Descriptive statistics

Standard deviation601.9046288
Coefficient of variation (CV)10.27995393
Kurtosis23779.52219
Mean58.55129632
Median Absolute Deviation (MAD)2
Skewness126.1856832
Sum5785922
Variance362289.1822
MonotonicityNot monotonic
2021-08-17T12:33:18.589405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036817
37.3%
18497
 
8.6%
25096
 
5.2%
33582
 
3.6%
42823
 
2.9%
52313
 
2.3%
61916
 
1.9%
71596
 
1.6%
81442
 
1.5%
91369
 
1.4%
Other values (1624)33367
33.8%
ValueCountFrequency (%)
036817
37.3%
18497
 
8.6%
25096
 
5.2%
33582
 
3.6%
42823
 
2.9%
52313
 
2.3%
61916
 
1.9%
71596
 
1.6%
81442
 
1.5%
91369
 
1.4%
ValueCountFrequency (%)
1299531
< 0.1%
621031
< 0.1%
396051
< 0.1%
392131
< 0.1%
340391
< 0.1%
326921
< 0.1%
293371
< 0.1%
231471
< 0.1%
226441
< 0.1%
150961
< 0.1%

Interactions

2021-08-17T12:32:53.040118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:53.221661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:53.371260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:53.530950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:53.688919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:53.849996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.018117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.180717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.335236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.503935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.685448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:54.856454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.009016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.149668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.294624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.434280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.588866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.732646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:55.876385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.020737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.182662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.333305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.495123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.664974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.824071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:56.997676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:57.163263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:57.327822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:57.492089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:57.663633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:57.830351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:58.000264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:58.185279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:58.687001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:59.161064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:59.324038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:59.480835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:59.634271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:59.958913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.128414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.283435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.430276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.596665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.757408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:00.907490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:01.063424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:01.217976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:01.451366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:33:09.354636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:33:09.716726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:09.891391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:10.069793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:33:10.583139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:10.753326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:10.916370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:11.078395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:11.242348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:11.415407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:11.587531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:11.768529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:12.177281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:12.361755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:12.542721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:12.715509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:12.896691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.063349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.223020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.384321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.541371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.712473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:13.892476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:33:14.069306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-17T12:33:19.053387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-17T12:33:19.297411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-17T12:33:19.536413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-17T12:33:19.777392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-17T12:33:14.337371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-17T12:33:14.697657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagegendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
0014male266.000000000
1114female6.000000000
2214male13.000000000
3314female93.000000000
4414male82.000000000
5514male15.000000000
6613male12.000000000
7713female0.000000000
8813male81.000000000
9913male171.000000000

Last rows

df_indexagegendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
988089899319male394.04538414445011508844355961669127
988099899420female402.01988332735110602572487333310332692
988109899520female699.03611973450777684414690993859
988119899624female182.0293812726018177655843117081756057
988129899728female290.022181618462610268429042503366018
988139899868female541.021183413996180893505118874916202
988149899918female21.01968172044011341243991059222820
988159900015female111.0200215241195912554119591146201092
988169900123female416.0256018545066516450657600756
988179900239female397.020497689410124439410953002913